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Kinematic Integration Network with Enhanced Temporal Intelligence and Quality-driven Attention for Precise Joint Angle Prediction in Exoskeleton-based Gait Analysis
  • Lyes Saad Saoud,
  • Irfan Hussain
Lyes Saad Saoud

Corresponding Author:[email protected]

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Irfan Hussain

Abstract

Exoskeleton robots hold tremendous promise in aiding the rehabilitation of patients with lower limb motor dysfunction. However, their effectiveness relies on the real-time and accurate prediction of knee joint angles. This study introduces KINETIQA (Kinematic Integration Network with Enhanced Temporal Intelligence and Quality-driven Attention), a novel model that significantly pushes the boundaries of knee angle prediction, offering substantial enhancements in clinical practice. The core strength of KINETIQA lies in its Kinematic Integration Network (KIN), a sophisticated architecture seamlessly integrating state-of-the-art techniques such as Transformers, temporal convolutions, and multi-headed attention mechanisms. This powerful synergy equips KINETIQA to capture the intricate dynamics of knee movement, both in the blink of an eye and over longer durations, resulting in a notable 20% reduction in prediction error compared to existing models. In addition to accuracy, KINETIQA employs rigorous feature selection and validation methods to guarantee clinical-grade precision. These processes offer clinicians crucial insights for tailored rehabilitation, injury prevention, and movement analysis. Our model's pioneering design integrates Transformers and temporal convolutions to capture detailed temporal dynamics of knee movement, leading to markedly enhanced prediction compared to current frameworks. These positive findings indicate that the proposed approach holds significant promise for application in exoskeleton robot control. The source code for KINETIQA is publicly available on GitHub at https://github.com/LyesSaadSaoud/KINETIQA/
09 Jan 2024Submitted to TechRxiv
18 Jan 2024Published in TechRxiv